Lithology identification method and device, electronic equipment and storage medium

Abstract

According to the lithology identification method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, the logging characteristic distribution of each lithology is summarized according to the response difference of different lithologies on the logging curve, the probability of existence of each lithology in different logging response value intervals is calculated, and the lithology identification accuracy is improved. And integrating a plurality of logging curves to obtain each lithology probability curve, and judging and determining the formation lithology according to a probability maximum principle. According to the lithology identification method for calculating the lithology probability curve based on the logging information provided by the embodiment of the invention, the lithology identification coincidence rate of the complex lithology carbonate reservoir by using the conventional logging information is improved, and a reliable data basis is provided for evaluation and feature description of the complex lithology reservoir.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and specifically to a lithology identification method, device, electronic equipment and storage medium. Background Art

[0002] The rock types of carbonate rocks are complex and varied, and the reservoir performance and physical properties of different lithological formations vary greatly. Taking a marine carbonate formation in North America as an example, the porosity and permeability of different lithological formations are significantly different. The weathered siliceous rock reservoir space is mainly composed of secondary dissolution pores, and fractures are not developed; the flint limestone reservoir space is mainly composed of secondary dissolution pores, and fractures are developed; the limestone reservoir space is mainly composed of fractures, and pores are not developed; the marlstone reservoir space has no cracks or pores; it can be seen that the degree of development of reservoir pores and fractures is closely related to the lithology, so lithology identification is very important for reservoir evaluation and oilfield development.

[0003] The general idea of ​​lithology identification is to establish various lithology identification modes by analyzing the logging response characteristics of the main lithology, and then use appropriate mathematical methods to establish a connection between lithology and logging data for lithology identification. According to the survey results of existing patents and literature, the methods used by predecessors in logging lithology identification include cross plot analysis, cluster analysis, discriminant analysis, principal component analysis, Fisher discriminant method, factor analysis, plate method, decision tree, BP neural network, SOM neural network, radial basis function neural network, support vector machine (SVM) and multi-layer perceptron (MLP), etc., each method has its own advantages and scope of application.

[0004] However, with the continuous deepening of oil and gas exploration and development, the characteristics of complex lithology and stratigraphy vary greatly, and traditional lithology identification technology is no longer applicable to the current research blocks. Therefore, it is necessary to explore new ideas and methods.

[0005] It should be pointed out that the information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or suggestion in any form that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0006] In view of this, the present application provides a lithology identification method, device, electronic device and storage medium to help solve the problems existing in the prior art.

[0007] In a first aspect, an embodiment of the present application provides a lithology identification method for calculating a lithology probability curve based on well logging data, comprising:

[0008] Performing standardization processing on multiple well logging curves respectively to obtain multiple standardized well logging curves;

[0009] Selecting a target logging curve from the plurality of standardized logging curves, wherein the target logging curve is a logging curve that has a better degree of differentiation between different lithologies;

[0010] Determining multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve;

[0011] Calculating the lithology probability of each lithology in each logging value interval in each target logging curve respectively;

[0012] Determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve;

[0013] The lithology of the target to be identified is determined according to the lithology probability of each lithology corresponding to the target to be identified.

[0014] In a possible implementation, the target logging curves include: a natural gamma ray GR logging curve, a density DEN logging curve, a neutron CNL logging curve, and a photoelectric absorption cross section PE logging curve.

[0015] In a possible implementation, the selecting a target well logging curve from the plurality of standardized well logging curves includes:

[0016] Statistically analyzing the distribution of logging curve values ​​of different lithologies in each of the standardized logging curves;

[0017] If there is one or more lithologies in any of the standardized well logging curves whose logging curve values ​​overlap in a small range or do not overlap, then any of the standardized well logging curves is determined to be a target well logging curve.

[0018] In a possible implementation, determining multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve includes:

[0019] According to the distribution and overlapping range of the logging values ​​of each lithology in each target logging curve, a plurality of logging value intervals of each target logging curve are determined.

[0020] In a possible implementation, respectively calculating the lithology probability of each lithology in each logging value interval in each target logging curve includes:

[0021] Respectively counting the frequency value of each lithology in each logging value interval in each target logging curve;

[0022] The lithology probability of each lithology in each logging value interval in each target logging curve is calculated according to the frequency value.

[0023] In a possible implementation, the calculation formula for the lithology probability of each lithology in each logging value interval is:

[0024]

[0025] Among them, P jk is the probability of the jth lithology in the kth logging value interval, X jk is the frequency value of the j-th lithology in the k-th logging value interval, m is the number of lithologies, 1≤j≤m.

[0026] In a possible implementation, determining the lithology probability of each lithology corresponding to the target to be identified according to the well logging value of the target to be identified in each target well logging curve and the lithology probability of each lithology in each well logging value interval in each target well logging curve includes:

[0027] Determine the lithology probability of each lithology corresponding to the target to be identified in each target logging curve according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve;

[0028] The average of the lithology probabilities corresponding to the target to be identified in the plurality of target logging curves is calculated to obtain the lithology probability of each lithology corresponding to the target to be identified.

[0029] In a possible implementation, the calculation formula for the lithology probability of each lithology corresponding to the target to be identified is:

[0030]

[0031] Among them, S j is the lithology probability of the jth lithology corresponding to the target to be identified, P ij is the lithology probability corresponding to the j-th lithology of the target to be identified in the i-th target logging curve, l is the number of target logging curves, 1≤i≤l.

[0032] In a possible implementation, determining the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified includes:

[0033] The lithology with the highest lithology probability corresponding to the target to be identified is determined as the lithology of the target to be identified.

[0034] In a second aspect, an embodiment of the present application provides a lithology identification device for calculating a lithology probability curve based on well logging data, comprising:

[0035] A standardization processing module is used to perform standardization processing on multiple logging curves respectively to obtain multiple standardized logging curves;

[0036] A target logging curve selection module is used to select a target logging curve from the plurality of standardized logging curves, wherein the target logging curve is a logging curve with better discrimination between different lithologies;

[0037] A logging value interval determination module, used to determine multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve;

[0038] A first lithology probability calculation module is used to respectively calculate the lithology probability of each lithology in each logging value interval in each target logging curve;

[0039] A second lithology probability calculation module is used to determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve;

[0040] The lithology determination module is used to determine the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0042] processor;

[0043] Memory;

[0044] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes any one of the methods described in the first aspect.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.

[0046] In the embodiment of the present application, based on the response differences of different lithologies on the logging curve, the logging characteristic distribution of each lithology is summarized, the probability of each lithology existing in different logging response value intervals is calculated, and the probability curve of each lithology is obtained by integrating multiple logging curves, and the formation lithology is determined based on the maximum probability principle. Compared with the intersection diagram, plate method and other methods, this method can use more than two logging curves to identify lithology; compared with the neural network, support vector machine and other methods, this method does not have a black box-like connection between lithology and logging data, and directly reflects the ability of each logging curve to distinguish lithology.

[0047] The lithology identification method based on the calculation of lithology probability curves based on logging data provided in the embodiment of the present application improves the lithology identification compliance rate of complex lithology carbonate reservoirs using conventional logging data, and provides a reliable data basis for the evaluation and feature description of complex lithology reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 A comparison diagram of the development of dissolution pores and fractures in reservoirs of different lithologies provided in an embodiment of the present application;

[0050] Figure 2 A schematic flow chart of a lithology identification method for calculating a lithology probability curve based on well logging data provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of a standardized processing of a GR logging curve provided in an embodiment of the present application;

[0052] Figure 4 A Box diagram of a PE value provided in an embodiment of the present application;

[0053] Figure 5 A comparison chart of the lithology identification results of a well logging and the lithology results of core analysis provided in the embodiment of the present application;

[0054] Figure 6 The present application also provides a structural block diagram of a lithology identification device for calculating lithology probability curves based on well logging data;

[0055] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0058] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0059] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0060] Taking a marine carbonate reservoir in North America as an example, the MSSP limestone layer in the study area is an unconventional carbonate oil and gas reservoir. The oil and gas are concentrated within a certain depth range below the Pre-Pennsylvanian unconformity surface and are distributed over a large area. The data show that the reservoir porosity distribution range of this block is wide, the permeability is low, and the lithology is complex. According to the analysis of the block's geological logging, core, and well logging data, the main lithologies are divided into five categories: weathered siliceous rock, flint limestone, marl, limestone, and mudstone. Figure 1 As shown in the figure, the porosity and permeability of different lithologies develop differently. The reservoir space of weathered siliceous rock is mainly composed of secondary dissolution pores, and fractures are not developed; the reservoir space of flint limestone is mainly composed of secondary dissolution pores, and fractures are developed; the reservoir space of limestone is mainly composed of fractures, and dissolution pores are not developed. It can be seen that lithology is closely correlated with reservoir performance. Therefore, lithology identification is very necessary in the process of reservoir evaluation.

[0061] The embodiment of the present application provides a lithology identification method for calculating lithology probability curves based on logging data. The method is based on the logging characteristic response analysis of various reservoir lithologies, preferably the logging curve used to identify the lithology, extracts the logging value characteristic distribution corresponding to the lithology, calculates the lithology probability curve, and comprehensively identifies the lithology by integrating the lithology probability calculation results on multiple logging curves, thereby improving the reservoir lithology identification compliance rate, providing reliable reservoir lithology information for oil and gas reservoir exploration and development, and reducing the risk of exploration and development evaluation. The following is a detailed description in conjunction with the specific implementation method.

[0062] See also Figure 2 , is a flow chart of a lithology identification method for calculating lithology probability curves based on well logging data provided in an embodiment of the present application. Figure 2 As shown, it mainly includes the following steps.

[0063] Step S201: performing standardization processing on a plurality of well logging curves respectively to obtain a plurality of standardized well logging curves.

[0064] Lithology identification is generally carried out by establishing a connection between actual lithology data and logging curve data and selecting an appropriate discrimination method, and then extending it to other wells. Therefore, in order to eliminate the systematic errors caused by measurement, logging curve standardization is required.

[0065] In the specific implementation, a stable sedimentation marker layer is selected, and the natural gamma ray GR logging curve, density DEN logging curve, neutron CNL logging curve, and photoelectric absorption cross section PE logging curve are standardized. The standardized logging curves are used as the data input for subsequent logging lithology identification. For example, the standardized processing process of the GR logging curve is as follows: Figure 3 shown.

[0066] Step S202: selecting a target logging curve from a plurality of standardized logging curves, where the target logging curve is a logging curve that has a better degree of differentiation for different lithologies.

[0067] In a possible implementation, the distribution of logging curve values ​​of different lithologies in each standardized logging curve can be statistically analyzed separately; if there is a small or no overlap in the logging curve values ​​of one or more lithologies in any standardized logging curve, any standardized logging curve is determined to be the target logging curve.

[0068] In the specific implementation, the logging curve value distribution histogram of the corresponding layer section of the same lithology can be statistically analyzed based on the core or logging data, and the distribution range of the logging values ​​displayed by the box plots of each lithology can be compared, and the histogram distribution diagram can be displayed in the form of a box, such as Figure 4 As shown. Among them, Figure 4 Different gray levels in the box represent different lithologies. The Box diagram can be used to compare the degree of overlap of the distribution ranges of different lithologies on a single logging curve value. If the overlap range for one or several lithologies is relatively small or non-overlapping, it means that the logging curve has a good discrimination for one or several lithologies and can be selected as the target logging curve; otherwise, it means that the logging curve will increase interference information and therefore cannot be selected as the target logging curve.

[0069] Based on the above screening principle, a target logging curve is selected from multiple standardized logging curves. For example, the target logging curves involved in the embodiments of the present application include natural gamma ray GR logging curve, density DEN logging curve, neutron CNL logging curve and photoelectric absorption cross section PE logging curve.

[0070] Step S203: determining a plurality of logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve.

[0071] In a possible implementation, multiple logging value intervals of each target logging curve may be determined according to the logging value distribution and overlapping range of each lithology in each target logging curve.

[0072] For example, according to Figure 4 The distribution and overlapping range of logging values ​​of each lithology in the PE logging curve are shown, and the logging value intervals of the PE logging curve are determined to be: 1-2.62, 2.62-3.1, 3.1-3.59, 3.59-4.5, 4.5-5.5.

[0073] Step S204: Calculate the lithology probability of each lithology in each logging value interval in each target logging curve.

[0074] In a possible implementation, the frequency value of each lithology in each logging value interval in each target logging curve may be counted respectively; and then the lithology probability of each lithology in each logging value interval in each target logging curve is calculated according to the frequency value.

[0075] In the specific implementation, the frequency distribution value of a certain lithology in the logging value interval can be expressed by X k (k=1, 2, 3...n) indicates that k represents the kth logging value interval. Among them, n represents the number of logging intervals, which can be understood.

[0076] In addition, for the frequency distribution of various lithologies on a certain logging curve, the lithology number is marked with j, and the frequency of the jth lithology can be recorded as: X jk (j = 1, 2, ... m, k = 1, 2, 3 ... n). Among them, m represents the number of lithology, which can be understood as follows:

[0077] For example, the frequency distribution of various lithologies in the PE value range is shown in Table 1 .

[0078] Table 1:

[0079]

[0080] Taking the frequency of limestone on the PE logging curve as an example, the frequency distribution in the PE value intervals of sequence numbers 1 to 5 is 0%, 0%, 0%, 7.9%, and 92.1%, respectively, totaling 100%. The total values ​​of other flint limestone, limestone, marl, and mudstone in each logging value interval are all 100%, which will not be repeated here for the sake of brevity.

[0081] After obtaining the frequency value of each lithology in each logging value interval in the target logging curve shown in Table 1, the lithology probability of each lithology in each logging value interval can be calculated based on the information in Table 1. In a possible implementation, the calculation formula of the lithology probability of each lithology in each logging value interval is shown in Formula 1.

[0082] Formula 1:

[0083]

[0084] Among them, P jk is the probability of the jth lithology in the kth logging value interval, X jk is the frequency value of the jth lithology in the kth logging value interval, m is the number of lithology, 1≤j≤m. It can be understood that

[0085] Taking the PE logging curve as an example, the probability calculation results of various lithologies in the PE value range are shown in Table 2.

[0086] Table 2:

[0087]

[0088] Referring to the above method, the lithology probability of each lithology in each logging value interval of the GR, RHOB and NPHI logging curves can be calculated respectively. For the sake of brevity, it will not be repeated here.

[0089] Step S205: Determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve.

[0090] In a possible implementation, the lithology probability of each lithology corresponding to the target to be identified in each target logging curve can be determined based on the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve; then the average of the lithology probabilities corresponding to the target to be identified in multiple target logging curves is calculated to obtain the lithology probability of each lithology corresponding to the target to be identified.

[0091] Taking limestone as an example, according to the logging value of the target to be identified, determine the logging interval corresponding to the limestone in the GR, PE, RHOB, and NPHI curves; then determine the lithology probability corresponding to the limestone in the GR, PE, RHOB, and NPHI curves; finally, calculate the average value of the lithology probability of the limestone in the GR, PE, RHOB, and NPHI curves to obtain the lithology probability of the limestone corresponding to the target to be identified. Based on the same method, the lithology probabilities of other lithologies can be calculated separately.

[0092] In a possible implementation, the calculation formula for the lithology probability of each lithology corresponding to the target to be identified is shown in Formula 2.

[0093] Formula 2:

[0094]

[0095] Among them, S j is the lithology probability of the jth lithology corresponding to the target to be identified, P ij is the lithology probability corresponding to the j-th lithology of the target to be identified in the i-th target logging curve, l is the number of target logging curves, 1≤i≤l.

[0096] Step S206: Determine the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified.

[0097] Specifically, the lithology probabilities of each lithology corresponding to the target to be identified are compared, and the lithology corresponding to the target to be identified with the highest lithology probability is used as the lithology of the target to be identified. For example, if the lithology probability of mudstone corresponding to the target to be identified is the highest, mudstone is used as the lithology of the target to be identified.

[0098] In a specific implementation, Formula 3 can be used to express it.

[0099] Formula 3:

[0100] Lith(j)=max(S j )

[0101] Where Lith(j) is the lithology of the target to be identified.

[0102] See also Figure 5 , which is a comparison chart of the lithology identification result of a well logging and the lithology result of the core analysis provided in the embodiment of the present application. Figure 5As shown in the figure, the 5th curve on the well logging diagram is the comprehensive lithology probability curve (normalized) calculated by various lithologies on the GR, PE, RHOB, and NPHI curves. When comparing the probability values ​​of various lithologies at the depth of 5281ft, the probability value of limestone lithology existence (POS_LIME) is the largest. According to the discrimination rule, the lithology at this depth is identified as limestone. By comparing the lithology identified by well logging with the lithology data of core analysis, the coincidence rate between the lithology of core analysis (track 6) and the lithology identified by this method (track 7) reaches 83.2%, which is significantly higher than the coincidence rate of 71.6% of the lithology identified by neural network (track 8).

[0103] In the embodiment of the present application, based on the response differences of different lithologies on the logging curve, the logging characteristic distribution of each lithology is summarized, the probability of each lithology existing in different logging response value intervals is calculated, and the probability curve of each lithology is obtained by integrating multiple logging curves, and the formation lithology is determined based on the maximum probability principle. Compared with the intersection diagram, plate method and other methods, this method can use more than two logging curves to identify lithology; compared with the neural network, support vector machine and other methods, this method does not have a black box-like connection between lithology and logging data, and directly reflects the ability of each logging curve to distinguish lithology.

[0104] The lithology identification method based on the calculation of lithology probability curves based on logging data provided in the embodiment of the present application improves the lithology identification compliance rate of complex lithology carbonate reservoirs using conventional logging data, and provides a reliable data basis for the evaluation and feature description of complex lithology reservoirs.

[0105] Corresponding to the above-mentioned embodiment, the present application also provides a lithology identification device for calculating lithology probability curves based on well logging data.

[0106] See also Figure 6 , which is a structural block diagram of a lithology identification device for calculating lithology probability curves based on well logging data provided by the present application. Figure 6 As shown, it mainly includes the following modules.

[0107] The standardization processing module 601 is used to perform standardization processing on multiple well logging curves respectively to obtain multiple standardized well logging curves;

[0108] A target logging curve selection module 602 is used to select a target logging curve from the plurality of standardized logging curves, wherein the target logging curve is a logging curve with better discrimination between different lithologies;

[0109] The logging value interval determination module 603 is used to determine multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve;

[0110] A first lithology probability calculation module 604 is used to respectively calculate the lithology probability of each lithology in each logging value interval in each target logging curve;

[0111] The second lithology probability calculation module 605 is used to determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve;

[0112] The lithology determination module 606 is used to determine the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified.

[0113] In the embodiment of the present application, based on the response differences of different lithologies on the logging curve, the logging characteristic distribution of each lithology is summarized, the probability of each lithology existing in different logging response value intervals is calculated, and the probability curve of each lithology is obtained by integrating multiple logging curves, and the formation lithology is determined based on the maximum probability principle. This solution improves the lithology identification compliance rate of complex lithology carbonate reservoirs using conventional logging data, and provides a reliable data basis for the evaluation and characteristic description of complex lithology reservoirs.

[0114] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0115] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0116] See also Figure 7 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device 700 may include: a processor 701, a memory 702 and a communication unit 703. These components communicate via one or more buses. Those skilled in the art may understand that the electronic device structure shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0117] The communication unit 703 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0118] The processor 701 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 702, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 701 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0119] The memory 702 is used to store the execution instructions of the processor 701. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0120] When the execution instructions in the memory 702 are executed by the processor 701 , the electronic device 700 is enabled to execute part or all of the steps in the above method embodiments.

[0121] Corresponding to the above embodiment, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program is running, the device where the computer-readable storage medium is located may be controlled to perform some or all of the steps in the above method embodiment. In a specific implementation, the computer-readable storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0122] Corresponding to the above-mentioned embodiment, the embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above-mentioned method embodiment.

[0123] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0124] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0127] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A lithology identification method for calculating lithology probability curves based on well logging data, characterized in that: include: Performing standardization processing on multiple well logging curves respectively to obtain multiple standardized well logging curves; Selecting a target logging curve from the plurality of standardized logging curves, wherein the target logging curve is a logging curve that has a better degree of differentiation between different lithologies; Determining multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve; Calculating the lithology probability of each lithology in each logging value interval in each target logging curve respectively; Determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve; The lithology of the target to be identified is determined according to the lithology probability of each lithology corresponding to the target to be identified.

2. The method according to claim 1, characterized in that The target logging curves include: natural gamma ray GR logging curve, density DEN logging curve, neutron CNL logging curve and photoelectric absorption cross section PE logging curve.

3. The method according to claim 1, characterized in that The step of selecting a target well logging curve from the plurality of standardized well logging curves comprises: Statistically analyzing the distribution of logging curve values ​​of different lithologies in each of the standardized logging curves; If there is one or more lithologies in any of the standardized well logging curves whose logging curve values ​​overlap in a small range or do not overlap, then any of the standardized well logging curves is determined to be a target well logging curve.

4. The method according to claim 1, characterized in that: Determining a plurality of logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve comprises: According to the distribution and overlapping range of the logging values ​​of each lithology in each target logging curve, a plurality of logging value intervals of each target logging curve are determined.

5. The method according to claim 1, characterized in that The respectively calculating the lithology probability of each lithology in each logging value interval in each target logging curve comprises: Respectively counting the frequency value of each lithology in each logging value interval in each target logging curve; The lithology probability of each lithology in each logging value interval in each target logging curve is calculated according to the frequency value.

6. The method according to claim 5, characterized in that The calculation formula for the lithology probability of each lithology within each logging value interval is: Among them, P jk is the probability of the jth lithology in the kth logging value interval, X jk is the frequency value of the j-th lithology in the k-th logging value interval, m is the number of lithologies, 1≤j≤m.

7. The method according to claim 1, characterized in that Determining the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve includes: Determine the lithology probability of each lithology corresponding to the target to be identified in each target logging curve according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve; The average of the lithology probabilities corresponding to the target to be identified in the plurality of target logging curves is calculated to obtain the lithology probability of each lithology corresponding to the target to be identified.

8. The method according to claim 7, characterized in that The calculation formula of the lithology probability of each lithology corresponding to the target to be identified is: Among them, S j is the lithology probability of the jth lithology corresponding to the target to be identified, P ij is the lithology probability corresponding to the j-th lithology of the target to be identified in the i-th target logging curve, l is the number of target logging curves, 1≤i≤l.

9. The method according to claim 1, characterized in that: The step of determining the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified includes: The lithology with the highest lithology probability corresponding to the target to be identified is determined as the lithology of the target to be identified.

10. A lithology identification device for calculating lithology probability curves based on well logging data, characterized in that: include: A standardization processing module is used to perform standardization processing on multiple logging curves respectively to obtain multiple standardized logging curves; A target logging curve selection module is used to select a target logging curve from the plurality of standardized logging curves, wherein the target logging curve is a logging curve with better discrimination between different lithologies; A logging value interval determination module, used to determine multiple logging value intervals of each target logging curve according to the logging characteristics of each lithology in each target logging curve; A first lithology probability calculation module is used to respectively calculate the lithology probability of each lithology in each logging value interval in each target logging curve; A second lithology probability calculation module is used to determine the lithology probability of each lithology corresponding to the target to be identified according to the logging value of the target to be identified in each target logging curve and the lithology probability of each lithology in each logging value interval in each target logging curve; The lithology determination module is used to determine the lithology of the target to be identified according to the lithology probability of each lithology corresponding to the target to be identified.

11. An electronic device, characterized in that: include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 9.

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